SoroushVahidi/scidocs huggingface dataset:
Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles
(2005)

This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.
Pilot Contamination and Precoding in Multi-Cell TDD Systems
(2011)

This paper considers a multi-cell multiple antenna system with precoding used at the base stations for downlink transmission. Channel state information (CSI) is essential for precoding at the base stations. An effective technique for obtaining this CSI is time-division duplex (TDD) operation where uplink training in conjunction with reciprocity simultaneously provides the base stations with downlink as well as uplink channel estimates. This paper mathematically characterizes the impact that uplink training has on the performance of such multi-cell multiple antenna systems. When non-orthogonal training sequences are used for uplink training, the paper shows that the precoding matrix used by the base station in one cell becomes corrupted by the channel between that base station and the users in other cells in an undesirable manner. This paper analyzes this fundamental problem of pilot contamination in multi-cell systems. Furthermore, it develops a new multi-cell MMSE-based precoding method that mitigates this problem. In addition to being linear, this precoding method has a simple closed-form expression that results from an intuitive optimization. Numerical results show significant performance gains compared to certain popular single-cell precoding methods.
Design of IRNSS receiver antennae for smart city applications in India
(2015)

In this research paper, a rectangular fractal antenna is designed which may be used for IRNSS based smart city applications in India. For tracking and positioning applications, the government of India has developed its own navigation system which is called Indian Regional Navigational Satellite System (IRNSS). To design an antenna for such an application is desirable. For the upcoming smart cities in India, the IRNSS will play a vital role in terms of traffic management, intelligent transportation, vehicle tracking and disaster management. A rectangular fractal antenna based on Sierpinski carpet antenna geometry for dual band resonant frequencies is simulated and tested. The antenna has operational frequencies at 2492.08 MHz (S-band) and 1176.45 MHz (L5-band) at return loss of -24.323 dB and -13.41dB respectively.
A Low-Cost Gate Driver Design Using Bootstrap Capacitors for Multilevel MOSFET Inverters
(2006)

Multilevel inverters require a large number of power semiconductors. The gate driver for each power semiconductor requires its own floating or isolated DC voltage source. Traditional methods on meeting the requirement are expensive and bulky. This paper presents a gate driver design for floating voltage source type multilevel inverters. Bootstrap capacitors are used to form the floating voltage sources in the design, which allows a single DC power supply to be used by all the gate drivers. Specially configured diode plus the proper charging cycles maintain adequate capacitor voltage levels. Such a simple and low cost solution allows user to utilize the easy accessible single-channel gate drivers for multilevel inverter applications without the extra cost on the bulky isolated DC power supplies for each gate driver. A prototype 3-cell 8-level floating voltage source inverter using the method illustrates the technique
Detecting Text in the Wild with Deep Character Embedding Network
(2019)

Most text detection methods hypothesize texts are horizontal or multi-oriented and thus define quadrangles as the basic detection unit. However, text in the wild is usually perspectively distorted or curved, which can not be easily tackled by existing approaches. In this paper, we propose a deep character embedding network (CENet) which simultaneously predicts the bounding boxes of characters and their embedding vectors, thus making text detection a simple clustering task in the character embedding space. The proposed method does not require strong assumptions of forming a straight line on general text detection, which provides flexibility on arbitrarily curved or perspectively distorted text. For character detection task, a dense prediction subnetwork is designed to obtain the confidence score and bounding boxes of characters. For character embedding task, a subnet is trained with contrastive loss to project detected characters into embedding space. The two tasks share a backbone CNN from which the multi-scale feature maps are extracted. The final text regions can be easily achieved by a thresholding process on character confidence and embedding distance of character pairs. We evaluated our method on ICDAR13, ICDAR15, MSRA-TD500, and Total Text. The proposed method achieves state-of-the-art or comparable performance on all of the datasets, and shows a substantial improvement in the irregular-text datasets, i.e. Total-Text.
Heterogeneous Transfer Learning for Image Classification
(2011)

Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related auxiliary domains for learning. While most of the existing works in this area are only focused on using the source data with the same representational structure as the target data, in this paper, we push this boundary further by extending a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text documents are arbitrary. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through matrix factorization, and to use the latent semantic features generated by the auxiliary data to build a better image classifier. We empirically verify the effectiveness of our algorithm on the Caltech-256 image dataset.
Employing U.S. Military Families to Provide Business Process Outsourcing Services: A Case study of Impact Sourcing and Reshoring
(2016)

This paper describes how a startup business process outsourcing (BPO) provider named Liberty Source helped a large U.S.-based client reshore business services from an established Indian BPO provider. Founded in 2014, Liberty Source is a for-profit firm that provides a competitive alternative to offshoring while fulfilling its social mission to launch and sustain the careers of U.S. military spouses and veterans who face various employment disadvantages. Thus, the case describes reshoring in the context of impact sourcing. It addresses key impact sourcing issues pertaining to workforce development, scalability, and impact on employees. The impact was positive: the workers found the employment and stable salary were beneficial, “the military” culture fit well with the workers, and workers received considerable flexibility and greater career options. Liberty Source was able to reduce a client’s costs after reshoring the client’s processes because Liberty Source’s U.S. site had about 20 percent fewer full time equivalents (FTEs) FTEs than the original India location and because Liberty Source received subsidies. We found evidence that the offshore BPO provider and Liberty source experienced difficulties with finding enough skilled staff for the wages offered and both firms experienced attrition problems, although attrition was greater in India.
A New Metrics for Countries' Fitness and Products' Complexity
(2012)

Classical economic theories prescribe specialization of countries industrial production. Inspection of the country databases of exported products shows that this is not the case: successful countries are extremely diversified, in analogy with biosystems evolving in a competitive dynamical environment. The challenge is assessing quantitatively the non-monetary competitive advantage of diversification which represents the hidden potential for development and growth. Here we develop a new statistical approach based on coupled non-linear maps, whose fixed point defines a new metrics for the country Fitness and product Complexity. We show that a non-linear iteration is necessary to bound the complexity of products by the fitness of the less competitive countries exporting them. We show that, given the paradigm of economic complexity, the correct and simplest approach to measure the competitiveness of countries is the one presented in this work. Furthermore our metrics appears to be economically well-grounded.
What's Wrong with the Diffusion of Innovation Theory
(2001)

This paper examines the usefulness of the diffusion of innovation research in developing theoretical accounts of the adoption of complex and networked IT solutions. We contrast six conjectures underlying DOI research with field data obtained from the study of the diffusion of EDI. Our analysis shows that DOI based analyses miss some important facets in the diffusion of complex technologies. We suggest that complex IT solutions should be understood as socially constructed and learning intensive artifacts, which can be adopted for varying reasons within volatile diffusion arenas. Therefore DOI researchers should carefully recognize the complex, networked, and learning intensive features of technology; understand the role of institutional regimes, focus on process features (including histories) and key players in the diffusion arena, develop multi-layered theories that factor out mappings between different layers and locales, use multiple perspectives including political models, institutional models and theories of team behavior, and apply varying time scales while crafting accounts of what happened and why. In general the paper calls for a need to develop DOI theories at the site by using multiple levels of
Computer-Mediated Communication Conflict Management in Groups that Work in Two Different Communication Contexts : Face-To-Face and
(2002)

can be found at: Small Group Research Additional services and information for http://sgr.sagepub.com/cgi/alerts  Email Alerts: http://sgr.sagepub.com/subscriptions  Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://sgr.sagepub.com/cgi/content/refs/33/5/481 SAGE Journals Online and HighWire Press platforms): (this article cites 36 articles hosted on the Citations
Static detection of cross-site scripting vulnerabilities
(2008)

Web applications support many of our daily activities, but they often have security problems, and their accessibility makes them easy to exploit. In cross-site scripting (XSS), an attacker exploits the trust a web client (browser) has for a trusted server and executes injected script on the browser with the server's privileges. In 2006, XSS constituted the largest class of newly reported vulnerabilities making it the most prevalent class of attacks today. Web applications have XSS vulnerabilities because the validation they perform on untrusted input does not suffice to prevent that input from invoking a browser's JavaScript interpreter, and this validation is particularly difficult to get right if it must admit some HTML mark-up. Most existing approaches to finding XSS vulnerabilities are taint-based and assume input validation functions to be adequate, so they either miss real vulnerabilities or report many false positives.
 This paper presents a static analysis for finding XSS vulnerabilities that directly addresses weak or absent input validation. Our approach combines work on tainted information flow with string analysis. Proper input validation is difficult largely because of the many ways to invoke the JavaScript interpreter; we face the same obstacle checking for vulnerabilities statically, and we address it by formalizing a policy based on the W3C recommendation, the Firefox source code, and online tutorials about closed-source browsers. We provide effective checking algorithms based on our policy. We implement our approach and provide an extensive evaluation that finds both known and unknown vulnerabilities in real-world web applications.
Combinatorial Bounds for Broadcast Encryption
(1998)

Abst rac t . A broadcast encryption system allows a center to communicate securely over a broadcast channel with selected sets of users. Each time the set of privileged users changes, the center enacts a protocol to establish a new broadcast key that only the privileged users can obtain, and subsequent transmissions by the center are encrypted using the new broadcast key. We study the inherent trade-off between the number of establishment keys held by each user and the number of transmissions needed to establish a new broadcast key. For every given upper bound on the number of establishment keys held by each user, we prove a lower bound on the number of transmissions needed to establish a new broad~ cast key. We show that these bounds are essentially tight, by describing broadcast encryption systems that come close to these bounds.
Database security - concepts, approaches, and challenges
(2005)

As organizations increase their reliance on, possibly distributed, information systems for daily business, they become more vulnerable to security breaches even as they gain productivity and efficiency advantages. Though a number of techniques, such as encryption and electronic signatures, are currently available to protect data when transmitted across sites, a truly comprehensive approach for data protection must also include mechanisms for enforcing access control policies based on data contents, subject qualifications and characteristics, and other relevant contextual information, such as time. It is well understood today that the semantics of data must be taken into account in order to specify effective access control policies. Also, techniques for data integrity and availability specifically tailored to database systems must be adopted. In this respect, over the years, the database security community has developed a number of different techniques and approaches to assure data confidentiality, integrity, and availability. However, despite such advances, the database security area faces several new challenges. Factors such as the evolution of security concerns, the "disintermediation" of access to data, new computing paradigms and applications, such as grid-based computing and on-demand business, have introduced both new security requirements and new contexts in which to apply and possibly extend current approaches. In this paper, we first survey the most relevant concepts underlying the notion of database security and summarize the most well-known techniques. We focus on access control systems, on which a large body of research has been devoted, and describe the key access control models, namely, the discretionary and mandatory access control models, and the role-based access control (RBAC) model. We also discuss security for advanced data management systems, and cover topics such as access control for XML. We then discuss current challenges for database security and some preliminary approaches that address some of these challenges.
Deep learning and process understanding for data-driven Earth system science
(2019)

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning. Complex Earth system challenges can be addressed by incorporating spatial and temporal context into machine learning, especially via deep learning, and further by combining with physical models into hybrid models.
Efficient mesh denoising via robust normal filtering and alternate vertex updating
(2017)

The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood (CAN) scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, we give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging rolling guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with few triangles flipped.
An Orbital Angular Momentum (OAM) Mode Reconfigurable Antenna for Channel Capacity Improvement and Digital Data Encoding
(2017)

For purpose of utilizing orbital angular momentum (OAM) mode diversity, multiple OAM beams should be generated preferably by a single antenna. In this paper, an OAM mode reconfigurable antenna is proposed. Different from the existed OAM antennas with multiple ports for multiple OAM modes transmitting, the proposed antenna with only a single port, but it can be used to transmit mode 1 or mode −1 OAM beams arbitrary by controlling the PIN diodes on the feeding network through a programmable microcontroller which control by a remote controller. Simulation and measurement results such as return loss, near-field and far-field radiation patterns of two operating states for mode 1 and mode −1, and OAM mode orthogonality are given. The proposed antenna can serve as a candidate for utilizing OAM diversity, namely phase diversity to increase channel capacity at 2.4 GHz. Moreover, an OAM-mode based encoding method is experimentally carried out by the proposed OAM mode reconfigurable antenna, the digital data are encoded and decoded by different OAM modes. At the transmitter, the proposed OAM mode reconfigurable antenna is used to encode the digital data, data symbol 0 and 1 are mapped to OAM mode 1 and mode −1, respectively. At the receiver, the data symbols are decoded by phase gradient method.
Unsupervised deformable image registration with fully connected generative neural network
(2018)

In this paper, a new deformable image registration method based on a fully connected neural network is proposed. Even though a deformation field related to the point correspondence between fixed and moving images are high-dimensional in nature, we assume that these deformation fields form a low dimensional manifold in many real world applications. Thus, in our method, a neural network generates an embedding of the deformation field from a low dimensional vector. This lowdimensional manifold formulation avoids the intractability associated with the high dimensional search space that most other methods face during image registration. As a result, while most methods rely on explicit and handcrafted regularization of the deformation fields, our algorithm relies on implicitly regularizing the network parameters. The proposed method generates deformation fields from latent low dimensional space by minimizing a dissimilarity metric between a fixed image and a warped moving image. Our method removes the need for a large dataset to optimize the proposed network. The proposed method is quantitatively evaluated using images from the MICCAI ACDC challenge. The results demonstrate that the proposed method improves performance in comparison with a moving mesh registration algorithm, and also it correlates well with independent manual segmentations by an expert.
A Low-Power Subthreshold to Above-Threshold Voltage Level Shifter
(2014)

This brief presents a power-efficient voltage level-shifter architecture that is capable of converting extremely low levels of input voltages to higher levels. In order to avoid the static power dissipation, the proposed structure uses a current generator that turns on only during the transition times, in which the logic level of the input signal is not corresponding to the output logic level. Moreover, the strength of the pull-up device is decreased when the pull-down device is pulling down the output node in order for the circuit to be functional even for the input voltage lower than the threshold voltage of a MOSFET. The operation of the proposed structure is also analytically investigated. Post-layout simulation results of the proposed structure in a 0.18-μm CMOS technology show that at the input low supply voltage of 0.4 V and the high supply voltage of 1.8 V, the level shifter has a propagation delay of 30 ns, a static power dissipation of 130 pW, and an energy per transition of 327 fJ for a 1-MHz input signal.
A robust, input voltage adaptive and low energy consumption level converter for sub-threshold logic
(2007)

A new level converter (LC) is proposed for logic voltage shifting between sub-threshold voltage to normal high voltage. By employing 2 PMOS diodes, the LC shows good operation robustness with sub-threshold logic input. The switching delay of the proposed LC can adapt with the input logic voltage which is more suitable for power aware systems. With a simpler circuit structure, the energy consumption of the LC is smaller than that of the existing sub-threshold LC. Simulation results demonstrate the performance improvement and energy reduction of the proposed LC. Test chip was fabricated using 0.18 mum CMOS process. Measurement results show that our proposed LC can operate correctly with an input at as low as 127 mV and an output voltage at 1.8V.